When you visit an online store, have you ever noticed how some products seem tailored to your interests? These product recommendations aren’t random. They’re designed to guide you to products you might like, boosting both your shopping experience and the store’s sales. For online businesses, knowing how to use eCommerce product recommendation effectively is key to keeping customers engaged and increasing revenue.
In this article, we will introduce you to 10 important insights into eCommerce product suggestions. We’ll cover different recommendation systems, strategies to improve customer engagement, and how automation can help you implement these recommendations more easily.
What is eCommerce Product Recommendation?
eCommerce product recommendation is personalized suggestions shown to customers while they shop online. These suggestions are based on their past behavior, browsing history, or similar customers’ actions. The goal is to show relevant products to shoppers, encouraging them to make additional purchases. Research shows that 75% of online shoppers prefer a personalized shopping experience, which is precisely what product recommendations aim to deliver. By using personalized suggestions, businesses can not only enhance the shopping experience but also increase conversion rates significantly.
Defining Product Recommendations
At its core, a product recommendation is a suggestion made to customers while they shop. It could appear as “People also bought this,” “Recommended for you,” or “You might like.” The aim is to make the customer feel like the store understands their needs and preferences.
Personalized product recommendations can boost revenue by 10-30% for eCommerce businesses that effectively implement them. These recommendations are powered by algorithms analyzing browsing behavior, purchase history, and similar customers’ actions, making them highly effective at influencing purchasing decisions
How They Work
Recommendation engines use customer data to create these suggestions. This could be anything from a shopper’s browsing history, previous purchases, or even how similar users behaved. As customers interact more with the site, machine learning product recommendations become smarter and more personalized.
- The engine tracks user behavior, such as products they click on, time spent on specific items, and what they add to their cart.
- Machine learning algorithms analyze this behavior and compare it to other users with similar tastes or habits.
- Based on the data, the system recommends products that are likely to interest the customer.
The longer a shopper uses the site, the more data is gathered, leading to better and more relevant suggestions.
Types of Product Recommendation Systems
Different eCommerce businesses use different types of recommendation engines. The type of system you choose depends on the kind of data you have and how personalized you want your recommendations to be.
Collaborative Filtering
Collaborative filtering looks at what similar customers have bought or viewed and recommends items based on those similarities. If customers A and B have similar tastes, and customer A buys a certain product, customer B might get a recommendation for the same product.
- This system works by analyzing patterns in customer behavior.
- It can suggest items based on what other similar users bought.
- There are two types: user-based collaborative filtering (comparing users) and item-based collaborative filtering (comparing products).
- It works well for larger eCommerce stores with lots of data.
- Collaborative filtering improves over time as more data is collected.
One downside of collaborative filtering is that it requires a lot of data to work well. Smaller stores might find that their recommendations improve only after they’ve collected enough customer interactions.
Content-Based Filtering
Content-based filtering looks at the product attributes themselves. Instead of comparing users, it compares the characteristics of products. If a customer likes a specific brand of shoes, content-based filtering will recommend other shoes from that brand or shoes with similar styles.
- Focuses on the attributes of products, such as brand, category, or style.
- Works well for new customers with little or no shopping history.
- Great for niche products where customer preferences are very specific.
- Recommendations don’t rely on other users’ data but on product characteristics.
- Ideal for businesses with well-organized product data.
Content-based filtering is especially useful for stores that want to promote specific products or brands. Since it doesn’t need user data, it’s also good for new or small eCommerce stores.
Hybrid Systems
Hybrid systems combine collaborative and content-based filtering to provide more accurate recommendations. These systems can look at both user behavior and product details, giving a more personalized experience for each customer.
- Combines data from users’ browsing and purchase history with product attributes.
- Offers more balanced and accurate recommendations.
- Takes advantage of both collaborative filtering’s ability to spot trends and content-based filtering’s ability to focus on details.
- Reduces cold-start problems (where there’s not enough data on a new user or product).
- Provides flexibility in recommendations, making them more dynamic and relevant.
Hybrid systems are typically the most effective, but they can be more complex to set up. However, for larger stores with diverse products and customers, they offer the best results.
Top 10 eCommerce Product Recommendation Insights
Now that you know how eCommerce product recommendation work, let’s explore 10 insights that can help you implement them effectively and grow your business.
Show Products Based on Browsing History
One of the easiest ways to offer personalized recommendations is by showing customers products they’ve already viewed. This helps them quickly find what they’re looking for and can lead to faster decisions.
- Show recently viewed items to remind customers of what interested them.
- Suggest similar products to those they’ve browsed, increasing their choices.
- Offer complementary products to enhance their shopping experience.
- Make browsing history accessible from the homepage or cart for easy reference.
- Use it to re-engage customers who left without buying.
Displaying products based on browsing history is a great way to keep your store relevant in the customer’s mind. It’s simple and effective for reminding shoppers about products they were considering but didn’t buy.
Bundle Commonly Bought Together Items
People love convenience. Offering product bundles that are often bought together is a great way to increase the value of each order.
- Bundle products that naturally go together, like a phone and a protective case.
- Show “Frequently bought together” items on product and cart pages.
- Offer a small discount for purchasing a bundle to make it more appealing.
- Suggest bundles that complement items in the cart.
- Include seasonal or holiday-specific bundles.
Bundles save customers time and encourage them to purchase more, boosting your average order value.
Use Personalized Recommendations in Emails
Emails are an excellent way to keep customers engaged, especially when they contain personalized product recommendations.
- Include product suggestions based on previous purchases.
- Send follow-up emails with related items after a customer makes a purchase.
- Use abandoned cart emails to remind customers of products they left behind.
- Personalize your newsletters with items they’ve shown interest in.
- Track which email recommendations lead to purchases to refine future emails.
Product recommendation emails feel more tailored to the customer and help drive them back to your store.
Introduce Shoppers to New Products
Customers are always looking for new and exciting products. Introducing them to fresh items can keep their shopping experience exciting.
- Highlight new arrivals on the homepage and category pages.
- Send emails showcasing the latest products in their favorite categories.
- Use personalized suggestions to introduce similar new products.
- Feature trending or hot items to create buzz.
- Offer early access to new products for loyal customers.
Keeping your product lineup fresh helps attract return visits from customers who want to see what’s new.
Bring Back Lost Sales
Sometimes, customers don’t complete their purchases. But with the right recommendations, you can win them back.
- Use retargeting ads to show customers the products they’ve viewed.
- Send reminder emails about items left in their cart.
- Offer discounts to encourage them to complete their purchase.
- Show recommended products that complement the items they’ve considered.
- Use urgency by highlighting low stock or limited-time offers.
By reaching out with personalized recommendations, you can recover lost sales and increase your conversion rates.
Display Related Products on Product Pages
When customers are viewing a product, they’re often open to suggestions for similar or related items.
- Display “Customers also bought” sections to suggest complementary products.
- Highlight accessories or add-ons related to the item.
- Use “Similar products” to offer alternatives.
- Show related items directly on the product page to keep them browsing.
- Offer a mix of best-sellers and lesser-known products to create interest.
Displaying related products on the page keeps customers engaged and encourages them to explore more options, leading to bigger purchases.
Use Social Proof to Drive Purchases
People trust the opinions of others. Showing reviews, ratings, and recommendations from other customers builds confidence.
- Highlight top-rated products with star ratings.
- Use “Best-reviewed” sections to showcase popular items.
- Display customer testimonials alongside recommended products.
- Show how many people have bought the product to build trust.
- Include social media content, like photos or reviews, to boost authenticity.
Social proof makes customers feel more secure in their purchases, increasing the likelihood that they’ll buy.
Highlight Best-Selling Items
Best-selling items are often popular for a reason. Highlighting these can encourage customers to pick what others love.
- Feature best-sellers on your homepage.
- Rotate best-selling items seasonally to keep things fresh.
- Use “Trending now” sections to show what’s currently popular.
- Include best-sellers in email marketing campaigns.
- Suggest top items from specific categories or collections.
By highlighting what’s already successful, you help customers find products they’re more likely to enjoy.
Offer Product Bundles with Discounts
Offering discounts on product bundles is a smart way to increase order value and make customers feel like they’re getting a deal.
- Create bundles with related items at a discounted price.
- Promote seasonal bundles for holidays or special events.
- Use limited-time offers to create urgency.
- Offer custom bundles where customers can pick their preferred items.
- Combine high-margin items with more affordable options for balance.
Discounted bundles encourage customers to spend more while feeling like they’re saving, leading to higher satisfaction.
Feature Highly Rated Products
Highly rated products often sell themselves. Featuring these items gives customers more confidence when making a purchase.
- Show top-rated products in a dedicated section.
- Use customer reviews and ratings to promote items.
- Rotate highly rated items based on real-time feedback.
- Encourage customers to review products to keep ratings current.
- Create categories for “customer favorites” or “top picks.”
Featuring highly rated products helps customers quickly find reliable and well-loved items, making their shopping experience easier.
Measuring the Success of Your Product Recommendation Strategy
Once you’ve implemented your eCommerce product recommendation, it’s essential to track how well they’re performing. This helps you optimize and improve your strategy over time.
Key Metrics to Track
There are several key metrics that can help you measure the success of your recommendations.
- Conversion rate: How many customers buy the recommended products.
- Average order value: The increase in total order size from recommended items.
- Click-through rate: How often customers click on recommended products.
- Customer satisfaction: Feedback from customers about the recommendations they receive.
- Return on investment (ROI): How much profit your recommendation engine generates compared to its cost.
Tracking these metrics allows you to see which types of recommendations work best and which need improvement.
A/B Testing for Continuous Improvement
A/B testing allows you to test different types of recommendations to see which ones are most effective.
- Test various recommendation placements, such as on product pages, in the cart, or in emails.
- Experiment with different recommendation types, like related products or best-sellers.
- Compare the performance of personalized recommendations versus more general ones.
- Adjust product bundles to see which combinations generate the most sales.
- Use data from your tests to continually refine your strategy.
By regularly testing and refining your approach, you can ensure that your recommendations stay relevant and effective.
How GritGlobal’s Atom8 Can Elevate Your eCommerce Product Recommendation
GritGlobal’s Atom8 – BigCommerce Automation can help automate your product recommendations, making them more efficient and targeted with eCommerce product recommendation
- Efficient Inventory Management: Automatically update stock levels and feature new or low-inventory products.
- Personalized Customer Segmentation: Group customers based on their shopping behavior for more personalized suggestions.
- Automated Cross-Selling and Upselling: Suggest complementary products at checkout to increase order size.
- Seamless Integration with Marketing Campaigns: Sync with platforms like Klaviyo to send personalized recommendations via email.
- Real-Time Updates: Adjust product recommendations in real-time based on user activity and stock levels.
- Enhanced User Experience: Reduce manual work and ensure recommendations are shown at key points, such as during checkout.
Atom8 from GritGlobal streamlines the process of recommending products, making it easier to offer customers relevant, timely suggestions that enhance their shopping experience.
Conclusion
eCommerce product recommendation is a powerful tool for increasing sales and improving customer satisfaction. By understanding how different recommendation systems work and applying key strategies, you can create a personalized shopping experience that keeps customers coming back. Whether it’s showing products based on browsing history, offering personalized email recommendations, or using social proof, the insights provided here will help you make the most of your product recommendations. And with tools like GritGlobal’s Atom8, you can automate and optimize these recommendations, driving more conversions with less manual effort. For more information on how GritGlobal can help your business, contact us today!
